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SUMMARY:Decomposition and Structured Model Reduction for Large Scale Syste
 ms Analysis - James Anderson\, University of Oxford
DTSTART:20160121T140000Z
DTEND:20160121T150000Z
UID:TALK63209@talks.cam.ac.uk
CONTACT:Tim Hughes
DESCRIPTION:In order to analyse or design a controller for a dynamical sys
 tem one often needs to first reduce the state dimension. In the realm of n
 etworked systems this is true even for Linear Time Invariant (LTI) systems
 . Traditional approaches to model reduction such as truncation and singula
 r perturbation require one to balance the model first\, thus introducing a
  coordinate transformation on the state variable. For networked systems\, 
 balancing annihilates the sparsity pattern in the system matrices\, and th
 erefore removes the inherent network structure. In this talk I will presen
 t a method for structure-preserving model reduction based on balance trunc
 ation. The method comes with error bounds and the reduction can be compute
 d using semidefinite programming in the worst case and linear algebra in t
 he best case. The algorithms developed are applied to mechanical and biolo
 gical systems and shown to outperform existing approaches.
LOCATION:Cambridge University Engineering Department\, LR5
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